AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.
Neural Information Processing Systems 30:1024–1034
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AGMARL-DKS: An Adaptive Graph-Enhanced Multi-Agent Reinforcement Learning for Dynamic Kubernetes Scheduling
AGMARL-DKS uses per-node multi-agent RL with GNN state representations and stress-aware lexicographical ordering to outperform the default Kubernetes scheduler on fault tolerance, utilization, and cost for batch and mission-critical workloads.